Instructions to use rootonchair/MiniMax-H3-nunchaku-lite-nvfp4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rootonchair/MiniMax-H3-nunchaku-lite-nvfp4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("rootonchair/MiniMax-H3-nunchaku-lite-nvfp4", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
MiniMax-H3 transformer, Nunchaku Lite nvfp4 (data-free SVDQuant)
The 30.8B MiniMax-H3 video+audio transformer, quantized with data-free SVDQuant (weight-span smoothing + rank-32 SVD low-rank branch + NVFP4 group quantization, group size 16, 362 quantized linears) - no calibration data. Packed in the Nunchaku Lite kernel layout; loads through diffusers' pre-quantized fast path.
This is the transformer component only - pair it with the other components from MiniMaxAI/MiniMax-H3:
import torch
from diffusers import MiniMaxH3Transformer3DModel
transformer = MiniMaxH3Transformer3DModel.from_pretrained(
"rootonchair/MiniMax-H3-nunchaku-lite-nvfp4", dtype=torch.bfloat16, device_map="cuda",
)
Requires the kernels package, DIFFUSERS_TRUST_REMOTE_KERNELS=true, and a
Blackwell-or-newer NVIDIA GPU (NVFP4 kernels need sm_120; PyTorch >= 2.7 with
CUDA >= 12.8).
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